Lee, R;
Griffiths, SL;
Gkoutos, GV;
Wood, SJ;
Bravo-Merodio, L;
Lalousis, PA;
Everard, L;
... Upthegrove, R; + view all
(2024)
Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model.
Schizophrenia Research
, 274
pp. 66-77.
10.1016/j.schres.2024.09.010.
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Abstract
Background: Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP). // Methods: Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures. // Results: The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56). // Conclusions: Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.
| Type: | Article |
|---|---|
| Title: | Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.schres.2024.09.010 |
| Publisher version: | http://dx.doi.org/10.1016/j.schres.2024.09.010 |
| Language: | English |
| Additional information: | Copyright © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Treatment resistance; Prediction; Modelling; Schizophrenia; FEP |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > Comprehensive CTU at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10197439 |
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